forked from kohya-ss/sd-scripts
-
Notifications
You must be signed in to change notification settings - Fork 1
/
train_db.py
488 lines (404 loc) · 21.2 KB
/
train_db.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
# DreamBooth training
# XXX dropped option: fine_tune
import gc
import argparse
import itertools
import math
import os
from multiprocessing import Value
import toml
from tqdm import tqdm
import torch
try:
import intel_extension_for_pytorch as ipex
if torch.xpu.is_available():
from library.ipex import ipex_init
ipex_init()
except Exception:
pass
from accelerate.utils import set_seed
from diffusers import DDPMScheduler
import library.train_util as train_util
import library.config_util as config_util
from library.config_util import (
ConfigSanitizer,
BlueprintGenerator,
)
import library.custom_train_functions as custom_train_functions
from library.custom_train_functions import (
apply_snr_weight,
get_weighted_text_embeddings,
prepare_scheduler_for_custom_training,
pyramid_noise_like,
apply_noise_offset,
scale_v_prediction_loss_like_noise_prediction,
)
# perlin_noise,
def train(args):
train_util.verify_training_args(args)
train_util.prepare_dataset_args(args, False)
cache_latents = args.cache_latents
if args.seed is not None:
set_seed(args.seed) # 乱数系列を初期化する
tokenizer = train_util.load_tokenizer(args)
# データセットを準備する
if args.dataset_class is None:
blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, False, False, True))
if args.dataset_config is not None:
print(f"Load dataset config from {args.dataset_config}")
user_config = config_util.load_user_config(args.dataset_config)
ignored = ["train_data_dir", "reg_data_dir"]
if any(getattr(args, attr) is not None for attr in ignored):
print(
"ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
", ".join(ignored)
)
)
else:
user_config = {
"datasets": [
{"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
]
}
blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
else:
train_dataset_group = train_util.load_arbitrary_dataset(args, tokenizer)
current_epoch = Value("i", 0)
current_step = Value("i", 0)
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
if args.no_token_padding:
train_dataset_group.disable_token_padding()
if args.debug_dataset:
train_util.debug_dataset(train_dataset_group)
return
if cache_latents:
assert (
train_dataset_group.is_latent_cacheable()
), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
# acceleratorを準備する
print("prepare accelerator")
if args.gradient_accumulation_steps > 1:
print(
f"gradient_accumulation_steps is {args.gradient_accumulation_steps}. accelerate does not support gradient_accumulation_steps when training multiple models (U-Net and Text Encoder), so something might be wrong"
)
print(
f"gradient_accumulation_stepsが{args.gradient_accumulation_steps}に設定されています。accelerateは複数モデル(U-NetおよびText Encoder)の学習時にgradient_accumulation_stepsをサポートしていないため結果は未知数です"
)
accelerator = train_util.prepare_accelerator(args)
# mixed precisionに対応した型を用意しておき適宜castする
weight_dtype, save_dtype = train_util.prepare_dtype(args)
# モデルを読み込む
text_encoder, vae, unet, load_stable_diffusion_format = train_util.load_target_model(args, weight_dtype, accelerator)
# verify load/save model formats
if load_stable_diffusion_format:
src_stable_diffusion_ckpt = args.pretrained_model_name_or_path
src_diffusers_model_path = None
else:
src_stable_diffusion_ckpt = None
src_diffusers_model_path = args.pretrained_model_name_or_path
if args.save_model_as is None:
save_stable_diffusion_format = load_stable_diffusion_format
use_safetensors = args.use_safetensors
else:
save_stable_diffusion_format = args.save_model_as.lower() == "ckpt" or args.save_model_as.lower() == "safetensors"
use_safetensors = args.use_safetensors or ("safetensors" in args.save_model_as.lower())
# モデルに xformers とか memory efficient attention を組み込む
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers, args.sdpa)
# 学習を準備する
if cache_latents:
vae.to(accelerator.device, dtype=weight_dtype)
vae.requires_grad_(False)
vae.eval()
with torch.no_grad():
train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
vae.to("cpu")
if torch.cuda.is_available():
torch.cuda.empty_cache()
gc.collect()
accelerator.wait_for_everyone()
# 学習を準備する:モデルを適切な状態にする
train_text_encoder = args.stop_text_encoder_training is None or args.stop_text_encoder_training >= 0
unet.requires_grad_(True) # 念のため追加
text_encoder.requires_grad_(train_text_encoder)
if not train_text_encoder:
accelerator.print("Text Encoder is not trained.")
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
text_encoder.gradient_checkpointing_enable()
if not cache_latents:
vae.requires_grad_(False)
vae.eval()
vae.to(accelerator.device, dtype=weight_dtype)
# 学習に必要なクラスを準備する
accelerator.print("prepare optimizer, data loader etc.")
if train_text_encoder:
# wightout list, adamw8bit is crashed
trainable_params = list(itertools.chain(unet.parameters(), text_encoder.parameters()))
else:
trainable_params = unet.parameters()
_, _, optimizer = train_util.get_optimizer(args, trainable_params)
# dataloaderを準備する
# DataLoaderのプロセス数:0はメインプロセスになる
n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1) # cpu_count-1 ただし最大で指定された数まで
train_dataloader = torch.utils.data.DataLoader(
train_dataset_group,
batch_size=1,
shuffle=True,
collate_fn=collator,
num_workers=n_workers,
persistent_workers=args.persistent_data_loader_workers,
)
# 学習ステップ数を計算する
if args.max_train_epochs is not None:
args.max_train_steps = args.max_train_epochs * math.ceil(
len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
)
accelerator.print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
# データセット側にも学習ステップを送信
train_dataset_group.set_max_train_steps(args.max_train_steps)
if args.stop_text_encoder_training is None:
args.stop_text_encoder_training = args.max_train_steps + 1 # do not stop until end
# lr schedulerを用意する TODO gradient_accumulation_stepsの扱いが何かおかしいかもしれない。後で確認する
lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
# 実験的機能:勾配も含めたfp16学習を行う モデル全体をfp16にする
if args.full_fp16:
assert (
args.mixed_precision == "fp16"
), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
accelerator.print("enable full fp16 training.")
unet.to(weight_dtype)
text_encoder.to(weight_dtype)
# acceleratorがなんかよろしくやってくれるらしい
if train_text_encoder:
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
)
else:
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
# transform DDP after prepare
text_encoder, unet = train_util.transform_if_model_is_DDP(text_encoder, unet)
if not train_text_encoder:
text_encoder.to(accelerator.device, dtype=weight_dtype) # to avoid 'cpu' vs 'cuda' error
# 実験的機能:勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
if args.full_fp16:
train_util.patch_accelerator_for_fp16_training(accelerator)
# resumeする
train_util.resume_from_local_or_hf_if_specified(accelerator, args)
# epoch数を計算する
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
# 学習する
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
accelerator.print("running training / 学習開始")
accelerator.print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
accelerator.print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
accelerator.print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
accelerator.print(f" num epochs / epoch数: {num_train_epochs}")
accelerator.print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
accelerator.print(
f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ(並列学習、勾配合計含む): {total_batch_size}"
)
accelerator.print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
accelerator.print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
global_step = 0
noise_scheduler = DDPMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
)
prepare_scheduler_for_custom_training(noise_scheduler, accelerator.device)
if args.zero_terminal_snr:
custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler)
if accelerator.is_main_process:
init_kwargs = {}
if args.log_tracker_config is not None:
init_kwargs = toml.load(args.log_tracker_config)
accelerator.init_trackers("dreambooth" if args.log_tracker_name is None else args.log_tracker_name, init_kwargs=init_kwargs)
loss_list = []
loss_total = 0.0
for epoch in range(num_train_epochs):
accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
current_epoch.value = epoch + 1
# 指定したステップ数までText Encoderを学習する:epoch最初の状態
unet.train()
# train==True is required to enable gradient_checkpointing
if args.gradient_checkpointing or global_step < args.stop_text_encoder_training:
text_encoder.train()
for step, batch in enumerate(train_dataloader):
current_step.value = global_step
# 指定したステップ数でText Encoderの学習を止める
if global_step == args.stop_text_encoder_training:
accelerator.print(f"stop text encoder training at step {global_step}")
if not args.gradient_checkpointing:
text_encoder.train(False)
text_encoder.requires_grad_(False)
with accelerator.accumulate(unet):
with torch.no_grad():
# latentに変換
if cache_latents:
latents = batch["latents"].to(accelerator.device)
else:
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * 0.18215
b_size = latents.shape[0]
# Get the text embedding for conditioning
with torch.set_grad_enabled(global_step < args.stop_text_encoder_training):
if args.weighted_captions:
encoder_hidden_states = get_weighted_text_embeddings(
tokenizer,
text_encoder,
batch["captions"],
accelerator.device,
args.max_token_length // 75 if args.max_token_length else 1,
clip_skip=args.clip_skip,
)
else:
input_ids = batch["input_ids"].to(accelerator.device)
encoder_hidden_states = train_util.get_hidden_states(
args, input_ids, tokenizer, text_encoder, None if not args.full_fp16 else weight_dtype
)
# Sample noise, sample a random timestep for each image, and add noise to the latents,
# with noise offset and/or multires noise if specified
noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
# Predict the noise residual
with accelerator.autocast():
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
if args.v_parameterization:
# v-parameterization training
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
target = noise
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
loss = loss.mean([1, 2, 3])
loss_weights = batch["loss_weights"] # 各sampleごとのweight
loss = loss * loss_weights
if args.min_snr_gamma:
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
if args.scale_v_pred_loss_like_noise_pred:
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
accelerator.backward(loss)
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
if train_text_encoder:
params_to_clip = itertools.chain(unet.parameters(), text_encoder.parameters())
else:
params_to_clip = unet.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=True)
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
train_util.sample_images(
accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet
)
# 指定ステップごとにモデルを保存
if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0:
accelerator.wait_for_everyone()
if accelerator.is_main_process:
src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
train_util.save_sd_model_on_epoch_end_or_stepwise(
args,
False,
accelerator,
src_path,
save_stable_diffusion_format,
use_safetensors,
save_dtype,
epoch,
num_train_epochs,
global_step,
accelerator.unwrap_model(text_encoder),
accelerator.unwrap_model(unet),
vae,
)
current_loss = loss.detach().item()
if args.logging_dir is not None:
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
if (
args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower()
): # tracking d*lr value
logs["lr/d*lr"] = (
lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
)
accelerator.log(logs, step=global_step)
if epoch == 0:
loss_list.append(current_loss)
else:
loss_total -= loss_list[step]
loss_list[step] = current_loss
loss_total += current_loss
avr_loss = loss_total / len(loss_list)
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
if args.logging_dir is not None:
logs = {"loss/epoch": loss_total / len(loss_list)}
accelerator.log(logs, step=epoch + 1)
accelerator.wait_for_everyone()
if args.save_every_n_epochs is not None:
if accelerator.is_main_process:
# checking for saving is in util
src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
train_util.save_sd_model_on_epoch_end_or_stepwise(
args,
True,
accelerator,
src_path,
save_stable_diffusion_format,
use_safetensors,
save_dtype,
epoch,
num_train_epochs,
global_step,
accelerator.unwrap_model(text_encoder),
accelerator.unwrap_model(unet),
vae,
)
train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
is_main_process = accelerator.is_main_process
if is_main_process:
unet = accelerator.unwrap_model(unet)
text_encoder = accelerator.unwrap_model(text_encoder)
accelerator.end_training()
if args.save_state and is_main_process:
train_util.save_state_on_train_end(args, accelerator)
del accelerator # この後メモリを使うのでこれは消す
if is_main_process:
src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
train_util.save_sd_model_on_train_end(
args, src_path, save_stable_diffusion_format, use_safetensors, save_dtype, epoch, global_step, text_encoder, unet, vae
)
print("model saved.")
def setup_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
train_util.add_sd_models_arguments(parser)
train_util.add_dataset_arguments(parser, True, False, True)
train_util.add_training_arguments(parser, True)
train_util.add_sd_saving_arguments(parser)
train_util.add_optimizer_arguments(parser)
config_util.add_config_arguments(parser)
custom_train_functions.add_custom_train_arguments(parser)
parser.add_argument(
"--no_token_padding",
action="store_true",
help="disable token padding (same as Diffuser's DreamBooth) / トークンのpaddingを無効にする(Diffusers版DreamBoothと同じ動作)",
)
parser.add_argument(
"--stop_text_encoder_training",
type=int,
default=None,
help="steps to stop text encoder training, -1 for no training / Text Encoderの学習を止めるステップ数、-1で最初から学習しない",
)
return parser
if __name__ == "__main__":
parser = setup_parser()
args = parser.parse_args()
args = train_util.read_config_from_file(args, parser)
train(args)